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July 9: Sparsity/Undersampling Tradeoffs in Compressed Sensing

Date: July 9 at 3pm

Speaker: Hatef Monajemi (Stanford)

Abstract: Compressed sensing (CS) is a sampling technique that exploits sparsity to speed up acquisition. An important question in CS is ``how much undersampling is allowed for a given level of sparsity?'' This question has been answered when sampling is done using Gaussian random matrices. Unfortunately, the theories for Gaussian matrices are not directly applicable to certain real life applications such as magnetic resonance imaging/spectroscopy where unique experimental considerations may impose extra constraints on the sampling matrix. In this talk, we will review the literature on sparsity/undersampling tradeoffs for Gaussian matrices and then present new predictions that are applicable to MR spectroscopy/imaging.

July 19: Knowthings

Date: July 9 at 11am (Note special time!)

Speaker: Knowthings

Abstract: TBA

 

TBD: On analyzing urban form at global scale with remote sensing data and generative adversarial networks

Date: TBDTBA

Speaker: Adrian Albert

Abstract: Current analyses of urban development use either simple, bottom-up models, that have limited predictive performance, or highly engineered, complex models relying on many sources of survey data that are typically scarce and difficult and expensive to collect. This talk presents work-in-progress developing a data-driven, flexible, non-parametric framework to simulate realistic urban forms using generative adversarial networks and planetary-level remote-sensing data. To train our urban simulator, we  curate and put forth a new dataset on urban form, integrating spatial distribution maps of population, nighttime luminosity, and built land densities, as well as best-available information on city administrative boundaries for 30,000 of the world's largest cities. This is the first analysis to date of urban form using modern generative models and remote-sensing data.

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